GraphSAGE-Based Traffic Speed Forecasting for Segment Network With Sparse Data

Abstract

Forecasting of traffic conditions plays a significant role in smart traffic management systems. With the prevalent use of massive vehicle trajectory data, agencies inevitably encounter missing data issues that hinder traffic flow forecasting in an urban road network. This paper studies the urban network-wide short-term forecasting of traffic speed with consideration to missing link speed data via (i) a data recovery algorithm to impute missing speed data for the segment network with nonlinear spatial and temporal correlations; and (ii) forecasting of spatially heterogeneous traffic speed within the road network using the GraphSAGE model. The influences of partially missing data and recovered data on the traffic speed forecasting are investigated. A case study of the urban area in Hangzhou, China, is presented, and it is found that the proposed recovery algorithm has the best performance in terms of traffic speed information reconstruction compared to benchmark methods. The case study also shows that using the recovered data acquires higher accuracy and efficiency in the short-term speed forecasting, compared to the case of using the original data without recovery. The proposed methods tackle missing traffic data issues and forecasting problems in the presence of missing data in an urban road network.